Estimate Geometric Transformation

Estimate geometric transformation from matching point pairs

  • Library:
  • Computer Vision Toolbox / Geometric Transformations

Description

Use the Estimate Geometric Transformation block to find the transformation matrix which maps the greatest number of point pairs between two images. A point pair refers to a point in the input image and its related point on the image created using the transformation matrix. You can select to use the RANdom SAmple Consensus (RANSAC) or the Least Median Squares algorithm to exclude outliers and to calculate the transformation matrix. You can also use all input points to calculate the transformation matrix.

Ports

Input

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Point coordinates, specified as an M-by-2 matrix of one-based [x y] point coordinates, where M represents the number of points.

The block outputs the same data type for the transformation matrix as the Pts1 and Pts 2 image points.

Data Types: single | double | int8 | int16 | int32 | uint8 | uint16 | uint32

Point coordinates, specified as an M-by-2 matrix of one-based [x y] point coordinates, where M represents the number of points.

The block outputs the same data type for the transformation matrix as the Pts1 and Pts 2 image points.

Data Types: single | double | int8 | int16 | int32 | uint8 | uint16 | uint32

Output

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Scalar value that represents the number of valid points in Pts1 and Pts 2.

Data Types: int8 | int16 | int32 | uint8 | uint16 | uint32

Transformation, returned as either a 3-by-2 or a 3-by-3 matrix. The block outputs the same data type for the transformation matrix as the Pts1 and Pts 2 image points.

Dependencies

  • When Pts1 and Pts 2 are single or double, the output transformation matrix will also have single or double data type.

  • When Pts1 and Pts 2 images are built-in integers, the option is available to set the transformation matrix data type to either Single or Double.

Data Types: single | double

Points used to calculate TForm, returned as an M-by-1 vector.

Dependencies

  • The Inlier port appears when you enable the Output Boolean signal indicating which point pairs are inliers parameter.

Data Types: Boolean

Parameters

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Specify the transformation type as either Nonreflective similarity, Affine, or Projective. See Transformations for a more detailed discussion.

Dependencies

You can set additional parameters depending on the transformation type:

  • For Projective transformation, you can specify a scalar algebraic distance threshold for determining inliers.

  • For Affine or Projective transformation, you can specify the distance threshold for determining inliers in pixels.

Enable to find and exclude outliers from the input points and use only the inlier points to calculate the transformation matrix. When you turn this parameter off, all input points are used to calculate the transformation matrix.

Select the method to find outliers as either RANdom SAmple Consensus (RANSAC) or Least Median of Squares See RANSAC and Least Median Squares Algorithms for a more detailed discussion.

Dependencies

This parameter appears when you enable the Find and exclude outliers check box.

Specify a scalar threshold value for determining inliers. The threshold controls the upper limit used to find the algebraic distance in the RANSAC algorithm.

Dependencies

This parameter appears when you set the Method parameter to Random Sample Consensus (RANSAC) and the Transformation type parameter to Projective.

Specify the upper limit distance a point can differ from the projection location of its corresponding point.

Dependencies

This parameter appears when you set the Method parameter to Random Sample Consensus (RANSAC) and you set the value of the Transformation type parameter to Nonreflective similarity or Affine.

Select Specified value to enter a positive integer value for the number of random samplings. Select Desired confidence to set the number of random samplings as a percentage and a maximum number.

Dependencies

This parameter appears when you select the Find and exclude outliers check box, and you set the value of the Method parameter to Random Sample Consensus (RANSAC).

Specify the number of random samplings for the algorithm to perform.

Dependencies

This parameter appears when you set the value of the Determine number of random samplings using parameter to Specified value.

Specify a percent desired confidence by entering a number between 0 and 100. The value represents the probability of the algorithm to find the largest group of points that can be mapped by a transformation matrix.

Dependencies

This parameter appears when you set the Determine number of random samplings using parameter to Desired confidence.

Specify an integer number for the maximum number of random samplings.

Dependencies

This parameter appears when you set the Method parameter to Random Sample Consensus (RANSAC) and you set the value of the Determine number of random samplings using parameter to Desired confidence.

Enable this parameter to stop random sampling when a percentage of input points have been found as inliers.

Dependencies

This parameter appears when you set the Method parameter to Random Sample Consensus (RANSAC).

Specify whether to perform refinement on the transformation matrix.

Dependencies

This parameter appears when you select Find and exclude outliers check box.

Enable this parameter to output the inlier point pairs that were used to calculate the transformation matrix.

Dependencies

  • This parameter appears when you select the Find and exclude outliers check box.

  • The block will not use this parameter with signed or double, data type points.

Specify transformation matrix data type as Single or Double when the input points are built-in integers.

Dependencies

The block will not use this parameter with signed or double, data type points.

Enable this parameter to allow variable-sized signal input.

Dependencies

Block Characteristics

Data Types

double | integer[a] | single

Multidimensional Signals

no

Variable-Size Signals

yes

[a] Generated code will be restricted to MATLAB host computers when you set the FFT implementation parameter to FFTW, or when the transform length is not a power of two.

Algorithms

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References

[1] R. Hartley and A. Ziserman, “Multiple View Geometry in Computer Vision,” Second edition, Cambridge University Press, 2003

Extended Capabilities

C/C++ Code Generation
Generate C and C++ code using Simulink® Coder™.

Fixed-Point Conversion
Design and simulate fixed-point systems using Fixed-Point Designer™.

See Also

Introduced in R2008a